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March 12, 2020 08:56
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linjieyangsc | |
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densecap | |
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densecap/models/dense_cap/train_cap_joint_inference.prototxt | |
linjie-yang more code cleaning; add demo with sample model | |
f76dd40 on 15 Jul 2017 | |
973 lines (949 sloc) 15 KB | |
name: "VGG_ILSVRC_16_layers" | |
layer { | |
name: 'input-data' | |
type: 'Python' | |
top: 'data' | |
top: 'im_info' | |
top: 'gt_boxes' | |
python_param { | |
module: 'roi_data_layer.layer' | |
layer: 'RoIDataLayer' | |
param_str: "'num_classes': 2" | |
} | |
} | |
layer { | |
name: "conv1_1" | |
type: "Convolution" | |
bottom: "data" | |
top: "conv1_1" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_1" | |
type: "ReLU" | |
bottom: "conv1_1" | |
top: "conv1_1" | |
} | |
layer { | |
name: "conv1_2" | |
type: "Convolution" | |
bottom: "conv1_1" | |
top: "conv1_2" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 64 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu1_2" | |
type: "ReLU" | |
bottom: "conv1_2" | |
top: "conv1_2" | |
} | |
layer { | |
name: "pool1" | |
type: "Pooling" | |
bottom: "conv1_2" | |
top: "pool1" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv2_1" | |
type: "Convolution" | |
bottom: "pool1" | |
top: "conv2_1" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_1" | |
type: "ReLU" | |
bottom: "conv2_1" | |
top: "conv2_1" | |
} | |
layer { | |
name: "conv2_2" | |
type: "Convolution" | |
bottom: "conv2_1" | |
top: "conv2_2" | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
decay_mult: 0 | |
} | |
convolution_param { | |
num_output: 128 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu2_2" | |
type: "ReLU" | |
bottom: "conv2_2" | |
top: "conv2_2" | |
} | |
layer { | |
name: "pool2" | |
type: "Pooling" | |
bottom: "conv2_2" | |
top: "pool2" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv3_1" | |
type: "Convolution" | |
bottom: "pool2" | |
top: "conv3_1" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_1" | |
type: "ReLU" | |
bottom: "conv3_1" | |
top: "conv3_1" | |
} | |
layer { | |
name: "conv3_2" | |
type: "Convolution" | |
bottom: "conv3_1" | |
top: "conv3_2" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_2" | |
type: "ReLU" | |
bottom: "conv3_2" | |
top: "conv3_2" | |
} | |
layer { | |
name: "conv3_3" | |
type: "Convolution" | |
bottom: "conv3_2" | |
top: "conv3_3" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
convolution_param { | |
num_output: 256 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu3_3" | |
type: "ReLU" | |
bottom: "conv3_3" | |
top: "conv3_3" | |
} | |
layer { | |
name: "pool3" | |
type: "Pooling" | |
bottom: "conv3_3" | |
top: "pool3" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4_1" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv4_1" | |
type: "Convolution" | |
bottom: "pool3" | |
top: "conv4_1" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_1" | |
type: "ReLU" | |
bottom: "conv4_1" | |
top: "conv4_1" | |
} | |
layer { | |
name: "conv4_2" | |
type: "Convolution" | |
bottom: "conv4_1" | |
top: "conv4_2" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv4_2" | |
type: "Convolution" | |
bottom: "conv4_1" | |
top: "conv4_2" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_2" | |
type: "ReLU" | |
bottom: "conv4_2" | |
top: "conv4_2" | |
} | |
layer { | |
name: "conv4_3" | |
type: "Convolution" | |
bottom: "conv4_2" | |
top: "conv4_3" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv4_3" | |
type: "Convolution" | |
bottom: "conv4_2" | |
top: "conv4_3" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu4_3" | |
type: "ReLU" | |
bottom: "conv4_3" | |
top: "conv4_3" | |
} | |
layer { | |
name: "pool4" | |
type: "Pooling" | |
bottom: "conv4_3" | |
top: "pool4" | |
pooling_param { | |
pool: MAX | |
kernel_size: 2 | |
stride: 2 | |
} | |
} | |
layer { | |
name: "conv5_1" | |
type: "Convolution" | |
bottom: "pool4" | |
top: "conv5_1" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv5_1" | |
type: "Convolution" | |
bottom: "pool4" | |
top: "conv5_1" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_1" | |
type: "ReLU" | |
bottom: "conv5_1" | |
top: "conv5_1" | |
} | |
layer { | |
name: "conv5_2" | |
type: "Convolution" | |
bottom: "conv5_1" | |
top: "conv5_2" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv5_2" | |
type: "Convolution" | |
bottom: "conv5_1" | |
top: "conv5_2" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_2" | |
type: "ReLU" | |
bottom: "conv5_2" | |
top: "conv5_2" | |
} | |
layer { | |
name: "conv5_3" | |
type: "Convolution" | |
bottom: "conv5_2" | |
top: "conv5_3" | |
param { | |
lr_mult: 0 | |
} | |
param { | |
lr_mult: 0 | |
} | |
include { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "conv5_3" | |
type: "Convolution" | |
bottom: "conv5_2" | |
top: "conv5_3" | |
param { | |
lr_mult: 0.1 | |
} | |
param { | |
lr_mult: 0.2 | |
} | |
exclude { stage: "freeze-convnet" } | |
convolution_param { | |
num_output: 512 | |
pad: 1 | |
kernel_size: 3 | |
} | |
} | |
layer { | |
name: "relu5_3" | |
type: "ReLU" | |
bottom: "conv5_3" | |
top: "conv5_3" | |
} | |
#========= RPN ============ | |
layer { | |
name: "rpn_conv/3x3" | |
type: "Convolution" | |
bottom: "conv5_3" | |
top: "rpn/output" | |
param { lr_mult: 1.0 } | |
param { lr_mult: 2.0 } | |
convolution_param { | |
num_output: 512 | |
kernel_size: 3 pad: 1 stride: 1 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
layer { | |
name: "rpn_relu/3x3" | |
type: "ReLU" | |
bottom: "rpn/output" | |
top: "rpn/output" | |
} | |
layer { | |
name: "rpn_cls_score" | |
type: "Convolution" | |
bottom: "rpn/output" | |
top: "rpn_cls_score" | |
param { lr_mult: 1.0 } | |
param { lr_mult: 2.0 } | |
convolution_param { | |
num_output: 24 # 2(bg/fg) * 12(anchors) | |
kernel_size: 1 pad: 0 stride: 1 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
layer { | |
name: "rpn_bbox_pred" | |
type: "Convolution" | |
bottom: "rpn/output" | |
top: "rpn_bbox_pred" | |
param { lr_mult: 1.0 } | |
param { lr_mult: 2.0 } | |
convolution_param { | |
num_output: 48 # 4 * 12(anchors) | |
kernel_size: 1 pad: 0 stride: 1 | |
weight_filler { type: "gaussian" std: 0.01 } | |
bias_filler { type: "constant" value: 0 } | |
} | |
} | |
layer { | |
bottom: "rpn_cls_score" | |
top: "rpn_cls_score_reshape" | |
name: "rpn_cls_score_reshape" | |
type: "Reshape" | |
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } | |
} | |
layer { | |
name: 'rpn-data' | |
type: 'Python' | |
bottom: 'rpn_cls_score' | |
bottom: 'gt_boxes' | |
bottom: 'im_info' | |
bottom: 'data' | |
top: 'rpn_labels' | |
top: 'rpn_bbox_targets' | |
top: 'rpn_bbox_inside_weights' | |
top: 'rpn_bbox_outside_weights' | |
python_param { | |
module: 'rpn.anchor_target_layer' | |
layer: 'AnchorTargetLayer' | |
param_str: "{'feat_stride': 16, 'scales': [4,8,16,32]}" | |
} | |
} | |
layer { | |
name: "rpn_loss_cls" | |
type: "SoftmaxWithLoss" | |
bottom: "rpn_cls_score_reshape" | |
bottom: "rpn_labels" | |
propagate_down: 1 | |
propagate_down: 0 | |
top: "rpn_cls_loss" | |
loss_weight: 0.1 | |
loss_param { | |
ignore_label: -1 | |
normalize: true | |
} | |
} | |
layer { | |
name: "rpn_loss_bbox" | |
type: "SmoothL1Loss" | |
bottom: "rpn_bbox_pred" | |
bottom: "rpn_bbox_targets" | |
bottom: 'rpn_bbox_inside_weights' | |
bottom: 'rpn_bbox_outside_weights' | |
top: "rpn_loss_bbox" | |
loss_weight: 0.05 | |
smooth_l1_loss_param { sigma: 3.0 } | |
} | |
#========= RoI Proposal ============ | |
layer { | |
name: "rpn_cls_prob" | |
type: "Softmax" | |
bottom: "rpn_cls_score_reshape" | |
top: "rpn_cls_prob" | |
} | |
layer { | |
name: 'rpn_cls_prob_reshape' | |
type: 'Reshape' | |
bottom: 'rpn_cls_prob' | |
top: 'rpn_cls_prob_reshape' | |
reshape_param { shape { dim: 0 dim: 24 dim: -1 dim: 0 } } | |
} | |
layer { | |
name: 'proposal' | |
type: 'Python' | |
bottom: 'rpn_cls_prob_reshape' | |
bottom: 'rpn_bbox_pred' | |
bottom: 'im_info' | |
top: 'rpn_rois' | |
# top: 'rpn_scores' | |
python_param { | |
module: 'rpn.proposal_layer' | |
layer: 'ProposalLayer' | |
param_str: "{'feat_stride': 16, 'scales':[4,8,16,32]}" | |
} | |
} | |
#layer { | |
# name: 'debug-data' | |
# type: 'Python' | |
# bottom: 'data' | |
# bottom: 'rpn_rois' | |
# bottom: 'rpn_scores' | |
# python_param { | |
# module: 'rpn.debug_layer' | |
# layer: 'RPNDebugLayer' | |
# } | |
#} | |
#single class proposal sampling layer, adapted from rpn.proposal_target_layer | |
layer { | |
name: 'roi-data' | |
type: 'Python' | |
bottom: 'rpn_rois' | |
bottom: 'gt_boxes' | |
top: 'rois' | |
top: 'labels' | |
top: 'bbox_targets' | |
top: 'bbox_inside_weights' | |
top: 'bbox_outside_weights' | |
top: 'clss' | |
python_param { | |
module: 'rpn.proposal_target_single_class_layer' | |
layer: 'ProposalTargetLayer' | |
param_str: "" | |
} | |
} | |
layer { | |
name: "silence" | |
type: "Silence" | |
bottom: "bbox_inside_weights" | |
bottom: "bbox_outside_weights" | |
} | |
#========= RCNN ============ | |
layer { | |
name: "roi_pool5" | |
type: "ROIPooling" | |
bottom: "conv5_3" | |
bottom: "rois" | |
top: "pool5" | |
roi_pooling_param { | |
pooled_w: 7 | |
pooled_h: 7 | |
spatial_scale: 0.0625 # 1/16 | |
} | |
} | |
layer { | |
name: "fc6" | |
type: "InnerProduct" | |
bottom: "pool5" | |
top: "fc6" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 4096 | |
} | |
} | |
layer { | |
name: "relu6" | |
type: "ReLU" | |
bottom: "fc6" | |
top: "fc6" | |
} | |
layer { | |
name: "drop6" | |
type: "Dropout" | |
bottom: "fc6" | |
top: "fc6" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "fc7" | |
type: "InnerProduct" | |
bottom: "fc6" | |
top: "fc7" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 4096 | |
} | |
} | |
layer { | |
name: "relu7" | |
type: "ReLU" | |
bottom: "fc7" | |
top: "fc7" | |
} | |
layer { | |
name: "drop7" | |
type: "Dropout" | |
bottom: "fc7" | |
top: "fc7" | |
dropout_param { | |
dropout_ratio: 0.5 | |
} | |
} | |
layer { | |
name: "cls_score" | |
type: "InnerProduct" | |
bottom: "fc7" | |
top: "cls_score" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 2 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
} | |
} | |
layer { | |
name: "fc8_new" | |
type: "InnerProduct" | |
bottom: "fc7" | |
top: "fc8" | |
param { | |
lr_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
} | |
inner_product_param { | |
num_output: 512 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
} | |
} | |
#retrieve sentences from region ids, newly implemented by Linjie | |
layer { | |
name: "id2sentence" | |
type: "Python" | |
bottom: "labels" | |
top: "input_sentence" | |
top: "target_sentence" | |
top: "cont_sentence" | |
top: "cont_bbox" | |
python_param { | |
module: 'rpn.sentence_data_layer' | |
layer: 'SentenceDataLayer' | |
param_str: "{'time_steps': 12, 'mode':'concat'}" | |
} | |
} | |
layer { | |
name: "embedding" | |
type: "Embed" | |
bottom: "input_sentence" | |
top: "embedded_input_sentence" | |
propagate_down: 0 | |
param { | |
lr_mult: 1 | |
} | |
embed_param { | |
bias_term: false | |
input_dim: 10001 | |
num_output: 512 | |
weight_filler {type: "gaussian" std: 0.01} | |
} | |
} | |
layer{ | |
name: "fc8_reshape" | |
type: "Reshape" | |
bottom: "fc8" | |
top: "fc8_reshape" | |
reshape_param { | |
shape { | |
dim: 1 | |
dim: -1 | |
dim: 512 | |
} | |
} | |
} | |
layer{ | |
name: "im_concat_words" | |
type: "Concat" | |
bottom:"fc8_reshape" | |
bottom:"embedded_input_sentence" | |
top: "im_concat_words" | |
concat_param { | |
axis: 0 | |
} | |
} | |
layer { | |
name: "lstm1" | |
type: "LSTM" | |
bottom: "im_concat_words" | |
bottom: "cont_sentence" | |
top: "lstm1" | |
propagate_down: 1 | |
propagate_down: 0 | |
recurrent_param { | |
num_output: 512 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
} | |
} | |
layer { | |
name: "lstm2" | |
type: "LSTM" | |
bottom: "im_concat_words" | |
bottom: "cont_sentence" | |
top: "lstm2" | |
propagate_down: 1 | |
propagate_down: 0 | |
recurrent_param { | |
num_output: 512 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
} | |
} | |
layer { | |
name: "bbox_pred" | |
type: "InnerProduct" | |
bottom: "lstm2" | |
top: "bbox_pred" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 4 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
axis: 2 | |
} | |
} | |
layer { | |
name: "loss_cls" | |
type: "SoftmaxWithLoss" | |
bottom: "cls_score" | |
bottom: "clss" | |
propagate_down: 1 | |
propagate_down: 0 | |
top: "loss_cls" | |
loss_weight: 0.1 | |
} | |
layer { | |
name: "cont_tile" | |
type: "Tile" | |
bottom: "cont_bbox" | |
top: "cont_tile" | |
tile_param { | |
axis: 2 | |
tiles: 4 | |
} | |
} | |
layer { | |
name: "bbox_tile" | |
type: "Tile" | |
bottom: "bbox_targets" | |
top: "bbox_tile" | |
tile_param { | |
axis: 0 | |
tiles: 12 | |
} | |
} | |
layer{ | |
name: "bbox_tile_reshape" | |
type: "Reshape" | |
bottom: "bbox_tile" | |
top: "bbox_tile_reshape" | |
reshape_param { | |
shape { | |
dim: 12 | |
dim: -1 | |
dim: 4 | |
} | |
} | |
} | |
layer { | |
name: "loss_bbox" | |
type: "SmoothL1Loss" | |
bottom: "bbox_pred" | |
bottom: "bbox_tile_reshape" | |
bottom: "cont_tile" | |
bottom: "cont_tile" | |
top: "loss_bbox" | |
loss_weight: 0.01 | |
} | |
layer { | |
name: "predict" | |
type: "InnerProduct" | |
bottom: "lstm1" | |
top: "predict" | |
param { | |
lr_mult: 1 | |
decay_mult: 1 | |
} | |
param { | |
lr_mult: 2 | |
decay_mult: 0 | |
} | |
inner_product_param { | |
num_output: 10001 | |
weight_filler {type: "gaussian" std: 0.01} | |
bias_filler {type: "constant" value: 0} | |
axis: 2 | |
} | |
} | |
layer { | |
name: "cross_entropy_loss" | |
type: "SoftmaxWithLoss" | |
bottom: "predict" | |
bottom: "target_sentence" | |
top: "cross_entropy_loss" | |
propagate_down: 1 | |
propagate_down: 0 | |
loss_weight: 1 | |
loss_param { | |
ignore_label: -1 | |
} | |
softmax_param { | |
axis: 2 | |
} | |
} | |
layer { | |
name: "accuracy" | |
type: "Accuracy" | |
bottom: "predict" | |
bottom: "target_sentence" | |
top: "accuracy" | |
include { phase: TEST } | |
accuracy_param { | |
axis: 2 | |
ignore_label: -1 | |
} | |
} | |
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